
import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
						raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
						raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
_snake_case      :							str     	=    logging.get_logger(__name__)

_snake_case      :							str     	=    "Hello, World!"
_snake_case      :							Tuple     	=    "en_XX"

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      Path("data_bin"						)
				lowercase         =      FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(_lowercase						).parent						)		,    checkpoint_file=Path(_lowercase						).name		,    _name="xmod_base"		,    arch="xmod_base"		,    task="multilingual_masked_lm"		,    data_name_or_path=str(_lowercase						)		,    bpe="sentencepiece"		,    sentencepiece_model=str(Path(_lowercase						).parent / "sentencepiece.bpe.model"						)		,    src_dict=str(data_dir / "dict.txt"						)		,    )
				xmod.eval()  # disable dropout
				print(_lowercase						)

				lowercase         =      xmod.model.encoder.sentence_encoder
				lowercase         =      XmodConfig(
				    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings		,    hidden_size=xmod.cfg.model.encoder_embed_dim		,    num_hidden_layers=xmod.cfg.model.encoder_layers		,    num_attention_heads=xmod.cfg.model.encoder_attention_heads		,    intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim		,    max_position_embeddings=514		,    type_vocab_size=1		,    layer_norm_eps=1e-5		,    pre_norm=xmod.cfg.model.encoder_normalize_before		,    adapter_reduction_factor=getattr(xmod.cfg.model		,    "bottleneck"		,    2						)		,    adapter_layer_norm=xmod.cfg.model.adapter_layer_norm		,    adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm		,    ln_before_adapter=xmod.cfg.model.ln_before_adapter		,    languages=xmod.cfg.model.languages		,    )
				if classification_head:
								lowercase         =      xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

				print("Our X-MOD config:"		,    _lowercase						)

				lowercase         =      XmodForSequenceClassification(_lowercase						) if classification_head else XmodForMaskedLM(_lowercase						)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				lowercase         =      xmod_sent_encoder.embed_tokens.weight
				lowercase         =      xmod_sent_encoder.embed_positions.weight
				lowercase         =      torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c xmod doesn't use them.

				lowercase         =      xmod_sent_encoder.layernorm_embedding.weight
				lowercase         =      xmod_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers						):
								# Encoder: start of layer
								lowercase         =      model.roberta.encoder.layer[i]
								lowercase         =      xmod_sent_encoder.layers[i]

								# self attention
								lowercase         =      layer.attention.self
								if not (
								    xmod_layer.self_attn.k_proj.weight.data.shape
								    == xmod_layer.self_attn.q_proj.weight.data.shape
								    == xmod_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)						)
								):
												raise AssertionError("Dimensions of self-attention weights do not match."						)

								lowercase         =      xmod_layer.self_attn.q_proj.weight
								lowercase         =      xmod_layer.self_attn.q_proj.bias
								lowercase         =      xmod_layer.self_attn.k_proj.weight
								lowercase         =      xmod_layer.self_attn.k_proj.bias
								lowercase         =      xmod_layer.self_attn.v_proj.weight
								lowercase         =      xmod_layer.self_attn.v_proj.bias

								# self-attention output
								lowercase         =      layer.attention.output
								if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
												raise AssertionError("Dimensions of self-attention output weights do not match."						)
								lowercase         =      xmod_layer.self_attn.out_proj.weight
								lowercase         =      xmod_layer.self_attn.out_proj.bias
								lowercase         =      xmod_layer.self_attn_layer_norm.weight
								lowercase         =      xmod_layer.self_attn_layer_norm.bias

								# intermediate
								lowercase         =      layer.intermediate
								if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError("Dimensions of intermediate weights do not match."						)
								lowercase         =      xmod_layer.fca.weight
								lowercase         =      xmod_layer.fca.bias

								# output
								lowercase         =      layer.output
								if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError("Dimensions of feed-forward weights do not match."						)
								lowercase         =      xmod_layer.fca.weight
								lowercase         =      xmod_layer.fca.bias
								lowercase         =      xmod_layer.final_layer_norm.weight
								lowercase         =      xmod_layer.final_layer_norm.bias
								if bert_output.adapter_layer_norm is not None:
												lowercase         =      xmod_layer.adapter_layer_norm.weight
												lowercase         =      xmod_layer.adapter_layer_norm.bias

								if sorted(bert_output.adapter_modules.keys()						) != sorted(xmod_layer.adapter_modules.keys()						):
												raise AssertionError("Lists of language adapters do not match."						)
								for lang_code, adapter in xmod_layer.adapter_modules.items():
												lowercase         =      bert_output.adapter_modules[lang_code]
												lowercase         =      xmod_layer.adapter_modules[lang_code]
												lowercase         =      from_adapter.fca.weight
												lowercase         =      from_adapter.fca.bias
												lowercase         =      from_adapter.fca.weight
												lowercase         =      from_adapter.fca.bias

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								lowercase         =      xmod_sent_encoder.layer_norm.weight
								lowercase         =      xmod_sent_encoder.layer_norm.bias

				if classification_head:
								lowercase         =      xmod.model.classification_heads["mnli"].dense.weight
								lowercase         =      xmod.model.classification_heads["mnli"].dense.bias
								lowercase         =      xmod.model.classification_heads["mnli"].out_proj.weight
								lowercase         =      xmod.model.classification_heads["mnli"].out_proj.bias
				else:
								# LM Head
								lowercase         =      xmod.model.encoder.lm_head.dense.weight
								lowercase         =      xmod.model.encoder.lm_head.dense.bias
								lowercase         =      xmod.model.encoder.lm_head.layer_norm.weight
								lowercase         =      xmod.model.encoder.lm_head.layer_norm.bias
								lowercase         =      xmod.model.encoder.lm_head.weight
								lowercase         =      xmod.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				lowercase         =      xmod.encode(_lowercase						).unsqueeze(0						)  # batch of size 1
				model.roberta.set_default_language(_lowercase						)

				lowercase         =      model(_lowercase						)[0]
				if classification_head:
								lowercase         =      xmod.model.classification_heads["mnli"](xmod.extract_features(_lowercase						)						)
				else:
								lowercase         =      xmod.model(_lowercase		,    lang_id=[SAMPLE_LANGUAGE]						)[0]
				print(our_output.shape		,    their_output.shape						)
				lowercase         =      torch.max(torch.abs(our_output - their_output						)						).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
				lowercase         =      torch.allclose(_lowercase		,    _lowercase		,    atol=1e-3						)
				print("Do both models output the same tensors?"		,    "🔥" if success else "💩"						)
				if not success:
								raise Exception("Something went wRoNg"						)

				Path(_lowercase						).mkdir(parents=_lowercase		,    exist_ok=_lowercase						)
				print(F'''Saving model to {pytorch_dump_folder_path}'''						)
				model.save_pretrained(_lowercase						)


if __name__ == "__main__":
						_snake_case      :							List[str]     	=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--xmod_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--classification_head", action="store_true", help="Whether to convert a final classification head."
						)
						_snake_case      :							List[str]     	=    parser.parse_args()
						convert_xmod_checkpoint_to_pytorch(
						    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)




"""simple docstring"""



from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class    __a						:
				pass